How OpenAI scrapes the web, and what developers should copy
Many developers look at how a lab like OpenAI collects web data and assume the right move is to copy the same approach for their own product. Some parts are worth copying. Many are not, because a lab building a language model and a team building a price feed or a jobs database are solving different problems.
This post breaks down how search-oriented and training-oriented crawling works, then looks at where that model stops being useful for teams that need clean, structured records.
How large-scale AI crawling actually works
The basic mechanics are older than LLMs. A crawler starts from a set of seed pages, such as encyclopedia entries or news front pages, downloads the HTML, pulls out every link, and repeats the process on those links. Over time this produces a very large snapshot of the public web. Open datasets built this way, like Common Crawl, sit underneath many language models.
OpenAI also runs its own crawler, GPTBot. According to public statements, it skips paywalled pages, skips sites that break its policies, and tries to avoid collecting personal data such as identity numbers or banking details. Content is often converted into a lighter text format like Markdown so it is easier to process downstream.
Cleaning matters more than collecting
The raw web contains spam, duplicated pages and machine-generated filler. Training pipelines therefore spend a lot of effort on:
Boilerplate removal: stripping menus, ads and layout tags to keep readable text.
Deduplication: removing near-identical pages so the same text is not counted many times.
Quality scoring: comparing text against trusted references like books and encyclopedias, and keeping what looks similar in vocabulary and structure.
Rendering, proxies and anti-bot defenses
JavaScript-heavy sites require a headless browser such as Playwright or Puppeteer to render the page before any content exists in the DOM. That is slower and heavier than plain HTTP requests. To avoid rate limits and IP bans, crawlers spread traffic across datacenter, residential or mobile proxies, and many scrapers add CAPTCHA solving and fingerprint handling on top.
Robots.txt and the legal side
Robots.txt is a convention, not a technical barrier. Established search engines and AI labs generally follow it, and GPTBot is documented as doing so. Legally, public data is usually fair game, but scraping behind logins can breach terms of service, and privacy laws like GDPR and CCPA restrict collecting personal data without consent. Copyright is still being argued in court, with the New York Times case against OpenAI being the most watched example.
Corpus crawling vs structured extraction
Here is the part most comparisons skip. A training crawler wants lots of reasonably clean text. A product team usually wants specific fields that are correct every single time.
Goal | Corpus crawling (AI labs) | Structured extraction (product teams) |
Output | Plain text or Markdown | JSON rows with named fields |
Tolerance for errors | Statistical, noise averages out | Low, one swapped price is a bug |
Unit of value | Billions of tokens | Each individual record |
Post-processing | LLM summaries, Q&A pairs, embeddings | Databases, dashboards, RAG indexes |
A model trained on slightly noisy text is still a good model. A database where the sale price and the original price were occasionally swapped is a broken database.
Where the LLM belongs in your pipeline
LLMs are now common at the extraction step itself: pass the page in, ask for JSON back. It works for small jobs. The friction shows up at volume. Language models predict likely tokens, so they can infer values that are not on the page, fill a missing field with a plausible default, or attach a date to the wrong label. Headless rendering plus an LLM call per page is also slower and more expensive than traditional parsing, which matches what most practitioners report.
Asking ChatGPT to write a BeautifulSoup script is another route. The generated code is a reasonable starting point, but it usually needs manual review and tends not to cope with dynamic content or strong bot protection.
A more durable pattern is to use deterministic extraction to get exact values, then put the LLM on top for summarization, classification or retrieval-augmented generation. That is the same separation the labs use: collection and cleaning first, model reasoning after.
A deterministic AI scraper for the extraction layer
Minexa.ai is a web scraping API built around that split. You train a scraper once in the Chrome extension by selecting the container that holds your data, and Minexa.ai discovers the fields, binds each column to a stable DOM selector, and returns the same JSON for the same HTML on every run. Missing values come back as null, not guesses. If a page does not match the trained structure, the API returns an error instead of quietly extracting something wrong. Rendering, proxies and anti-bot handling are part of the same request.
The workflow for developers is short:
Install the extension and open a list or detail page with the data visible.
Click Advanced Scenarios, choose List Mode or Detail Mode, then Continue.
Confirm the highlighted container and click Create Scraper.
Click API Request, pick a scraping config in the Python tab, and copy the code.
Swap in other URLs with the same structure and run it.
You can install the Minexa Chrome extension for developers to train your first scraper. A request to the data endpoint looks like this:
POST https://api.minexa.ai/data/
{
"batches": [{
"scraper_id": 6418,
"columns": ["top_20"],
"urls": ["https://example.com/jobs?page=2"],
"scraping": {"js_render": true, "timeout": 30, "provider": "service3", "proxy": "verified", "retry": 3}
}],
"threads": 3
}If you already fetch HTML with your own crawler and proxies, pass those stored files through file_urls and only the extraction runs. That keeps your existing collection stack and replaces just the fragile parsing step.
What to take from the labs
Respect robots.txt and avoid personal data by default.
Budget for rendering and proxies on dynamic sites.
Clean and validate before anything reaches a model or a database.
Keep extraction exact, and let the LLM reason over clean data afterwards.
To see the full setup, the developer getting started guide walks through list and detail scrapers end to end.
Related reading: Where should the LLM sit in a scraping pipeline?
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